北疆地区草地TI-NDVI与NDVImax时空异质性评价

doi:10.13866/j.azr.2022.04.16

干旱区研究 ›› 2022, Vol. 39 ›› Issue (4): 1155-1165.doi: 10.13866/j.azr.2022.04.16

北疆地区草地TI-NDVI与NDVImax时空异质性评价

焦阿永¹(),陈伏龙¹,闫俊杰²(),凌红波^3,⁴,申瑞华¹

1.石河子大学水利建筑工程学院,新疆石河子 832003
2.伊犁师范大学资源与生态研究所,新疆伊宁 835000
3.中国科学院新疆生态与地理研究所,新疆乌鲁木齐 830011
4.中国科学院大学,北京 100049

收稿日期:2021-11-20 修回日期:2022-02-28 出版日期:2022-07-15 发布日期:2022-09-26
通讯作者: 闫俊杰
作者简介:焦阿永（1996-）,男,硕士研究生,主要从事生态水文过程研究. E-mail: 943573243@qq.com
基金资助:
中国科学院“西部青年学者”项目(2019-XBQNXZ-A-001);新疆天山青年计划(2019Q006);中国科学院科技服务网络计划（STS 计划）项目(KFJ-STS-QYZD-114)

Spatio-temporal heterogeneity evaluation of grassland TI-NDVI and NDVImax in northern Xinjiang

JIAO Ayong¹(),CHEN Fulong¹,YAN Junjie²(),LING Hongbo^3,⁴,SHEN Ruihua¹

1. College of Water Conservancy & Architectural Engineering, Shihezi University, Shihezi 832003, Xinjiang, China
2. Institute of Resources and Ecology, Yili Normal University, Yining 835000, Xinjiang, China
3. Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Urumqi 830011, Xinjiang, China
4. University of Chinese Academy of Science, Beijing 100049, China

Received:2021-11-20 Revised:2022-02-28 Online:2022-07-15 Published:2022-09-26
Contact: Junjie YAN

摘要/Abstract

摘要：

选择草地类型丰富多样的北疆作为研究区。基于MODIS NDVI数据,获取时间累积归一化植被指数TI-NDVI和年最大NDVI(NDVImax),借助GIS空间分析技术、变异系数（C_V）及Mann-Kendall非参数趋势检验等方法,对2000—2019年北疆地区草地覆盖动态变化进行了分析,并探究了TI-NDVI和NDVImax对草地时空异质性的表达能力的比较优势。结果表明：（1）用NDVImax和TI-NDVI表征的北疆草地呈现明显海拔分异。TI-NDVI总体随NDVImax的增大而增大,但NDVImax或TI-NDVI相同的区域,其TI-NDVI或NDVImax却存在较大差异。（2） 2000—2019年北疆地区草地TI-NDVI和NDVImax总体呈显著增加趋势（P<0.01）,但草地TI-NDVI变化的空间分异与NDVImax明显不同,全区17.55%的草地TI-NDVI变化趋势与NDVImax变化趋势相反。尤其阿尔泰山与伊犁河谷,高覆盖草地分布区的NDVImax与TI-NDVI均呈相反变化趋势。（3）在时间和空间维度上,北疆山区高覆盖草地TI-NDVI的C_V均高于NDVImax。TI-NDVI对高覆盖草地的时空异质性反映更敏感,能在一定程度上削弱草地动态评价中NDVI光饱和缺陷的影响。

关键词: 时空异质性, TI-NDVI, NDVImax, 草地, 北疆

Abstract:

Grassland change is an important component of global change, which has attracted considerable attention. The temporal and spatial heterogeneity of grassland dynamics is the main concern in evaluating grassland dynamics. Northern Xinjiang, which is characterized with diverse grassland types, was selected as the research area. In this study, we calculated the time-integrated normalized vegetation index (TI-NDVI) and annual maximum NDVI (NDVImax) on the basis of the MODIS NDVI data. Using spatial analysis technology of GIS, mathematical statistical methods of coefficient of variation (C_V), and Mann-Kendall non-parametric statistics, the dynamic changes of grassland in northern Xinjiang were analyzed from 2000 to 2019, and the comparative advantages of TI-NDVI and NDVImax in expressing the temporal and spatial heterogeneity of grassland were explored. Results indicated that (1) the grasslands in northern Xinjiang, characterized by NDVImax and TI-NDVI, showed evident altitudinal differentiation. In general, the TI-NDVI increased with the increase of NDVImax. However, the areas with the same NDVImax or TI-NDVI showed great differences in TI-NDVI or NDVImax. (2) From 2000 to 2019, the grassland TI-NDVI and NDVImax in the northern Xinjiang showed a significant increasing trend (P<0.01), but the spatial differentiation of the changing trends of TI-NDVI was different from that of NDVImax. 17.55% of the grassland in northern Xinjiang showed opposite changing trends in TI-NDVI and NDVImax. For Altai Mountains and the mountains around Ili Valley, which are characterized with grassland of high coverage, the NDVImax and TI-NDVI showed opposite changing trends. (3) The C_V of TI-NDVI was higher than NDVImax in temporal and spatial dimensions in grassland with high coverage in northern Xinjiang. Furthermore, TI-NDVI was more sensitive to the temporal and spatial heterogeneity of high-coverage grassland, which can weaken the influence of saturation defect of NDVI in grassland dynamic evaluation to a certain extent.

Key words: spatio-temporal heterogeneity, TI-NDVI, NDVImax, grassland, northern Xinjiang

焦阿永,陈伏龙,闫俊杰,凌红波,申瑞华. 北疆地区草地TI-NDVI与NDVImax时空异质性评价[J]. 干旱区研究, 2022, 39(4): 1155-1165.

JIAO Ayong,CHEN Fulong,YAN Junjie,LING Hongbo,SHEN Ruihua. Spatio-temporal heterogeneity evaluation of grassland TI-NDVI and NDVImax in northern Xinjiang[J]. Arid Zone Research, 2022, 39(4): 1155-1165.

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参考文献 42

[1]	Jiang L L, Jiapaer G, Bao A M, et al. Vegetation dynamics and responses to climate change and human activities in Central Asia[J]. Science of the Total Environment, 2017, 599: 967-980.
[2]	陈京华, 贾文雄, 赵珍, 等. 1982—2006年祁连山植被覆盖的时空变化特征研究[J]. 地球科学进展, 2015, 30(7): 834-845. doi: 10.11867/j.issn.1001-8166.2015.07.0834
	[Chen Jinghua, Jia Wenxiong, Zhao Zhen, et al. Research on temporal and spatial variation characteristics of vegetation cover of Qilian Mountains from 1982 to 2006[J]. Advances in Earth Science, 2015, 30(7): 834-845.] doi: 10.11867/j.issn.1001-8166.2015.07.0834
[3]	Alexander M R H, Shane W C, Michael L R. The use of time-integrated NOAA NDVI data and rainfall to assess landscape degradation in the arid shrubland of western Australia[J]. Remote Sensing of Environment, 2003, 85: 145-158. doi: 10.1016/S0034-4257(02)00199-2
[4]	张远东, 张笑鹤, 刘世荣. 西南地区不同植被类型归一化植被指数与气候因子的相关分析[J]. 应用生态学报, 2011, 22(2): 323-330. pmid: 21608242
	[Zhang Yuandong, Zhang Xiaohe, Liu Shirong. Correlation analysis on normalized difference vegetation index (NDVI) of different vegetations and climatic factors in Southwest China[J]. Chinese Journal of Applied Ecology, 2011, 22(2): 323-330.] pmid: 21608242
[5]	闫俊杰, 陈晨, 赵阳, 等. 基于TINDVI的伊犁河谷草地覆盖变化[J]. 水土保持研究, 2021, 28(3): 331-339.
	[Yan Junjie, Chen Chen, Zhao Yang, et al. Dynamics of grassland coverage in Ili River valley based on TINDVI[J]. Research of Soil and Water Conservation, 2021, 28(3): 331-339.]
[6]	郭继凯, 吴秀芹, 董贵华, 等. 基于MODIS/NDVI的塔里木河流域植被覆盖变化驱动因素相对作用分析[J]. 干旱区研究, 2017, 34(3): 621-629.
	[Guo Jikai, Wu Xiuqin, Dong Guihua, et al. Vegetation coverage change and relative effects of driving factors based on MODIS /NDVI in the Tarim River Basin[J]. Arid Zone Research, 2017, 34(3): 621-629.]
[7]	Reeves M C, Zhao M S, Running S W. Applying improved estimates of MODIS productivity to characterize grassland vegetation dynamics[J]. Rangeland Ecology & Management, 2006, 59: 1-10. doi: 10.2111/1551-5028(2006)59[001:AIEOMP]2.0.CO;2
[8]	Gitelson A A. Wide dynamic range vegetation index for remote quantification of biophysical characteristics of vegetation[J]. Journal of Plant Physiology, 2004, 161: 165-173. pmid: 15022830
[9]	鲁泽恩, 田玉刚, 柳庆威, 等. 基于Sentinel-1和DEM数据的南岭高植被覆盖区地形线性特征提取方法[J]. 地球科学, 2021, 46(4): 1349-1358.
	[Lu Ze’en, Tian Yugang, Liu Qingwei, et al. Topographical linear feature extraction method based on Sentinel-1 and DEM in areas with high vegetation coverage of Nanling[J]. Earth Science, 2021, 46(4): 1349-1358.]
[10]	刘佳丽, 范建容, 张茜彧, 等. 高寒草地生长季/非生长季植被盖度遥感反演[J]. 草业学报, 2021, 30(9): 15-26.
	[Liu Jiali, Fan Jianrong, Zhang Qianyu, et al. Remote sensing estimation of vegetation cover in alpine grassland in the growing and non-growing seasons[J]. Acta Prataculturae Sinica, 2021, 30(9): 15-26.]
[11]	Holben B N. Characteristics of maximum-value composite images from temporal AVHRR data[J]. International Journal of Remote Sensing, 1986, 7: 1417-1434. doi: 10.1080/01431168608948945
[12]	Gutman G, Ignatov A. The derivation of the green vegetation fraction from NOAA/AVHRR data for use in numerical weather prediction models[J]. International Journal of Remote Sensing, 1998, 19: 1533-1543. doi: 10.1080/014311698215333
[13]	Hill M J, Donald G E. Estimating spatio-temporal patterns of agricultural productivity in fragmented landscapes using AVHRR NDVI time series[J]. Remote Sensing of Environment, 2003, 84: 367-384. doi: 10.1016/S0034-4257(02)00128-1
[14]	杨鑫, 曹文侠, 鱼小军, 等. 基于近20年MODIS NDVI日数据的青海省草地资源动态监测及其对环境因子的响应[J]. 草业学报, 2021, 30(9): 1-14.
	[Yang Xin, Cao Wenxia, Yu Xiaojun, et al. Dynamic monitoring of grassland resources and their responses to environmental factors in Qinghai Province based on analyses of daily MODIS NDVI data from the past 20 years[J]. Acta Prataculturae Sinica, 2021, 30(9): 1-14.]
[15]	Cuomo V, Lanfredi M, Lasaponara R. Detection of interannual variation of vegetation in middle and southern Italy during 1985-1999 with 1 km NOAA AVHRR NDVI data[J]. Journal of Geophysical Research: Earth Surface, 2001, 106: 17863-17876.
[16]	Ferrara R, Costanza F, Nicola M, et al. Comparison of different ground-based NDVI measurement methodologies to evaluate crop biophysical properties[J]. Italian Journal of Agronomy, 2010, 5: 145-154. doi: 10.4081/ija.2010.145
[17]	刘珞丹, 李晶, 柳彩霞, 等. 2000—2015年长江经济带植被覆盖时空变化特征及影响因素分析[J]. 水土保持研究, 2021, 28(6): 330-336.
	[Liu Luodan, Li Jing, Liu Caixia, et al. Analysis on the characteristics of temporal and spatial changes and influencing factors of vegetation coverage in the Yangtze River Economic Belt from 2000 to 2015[J]. Research of Soil and Water Conservation, 2021, 28(6): 330-336.]
[18]	Goward S N, Tucker C J, Dye D G. North American vegetation patterns observed with the NOAA-7 advanced very high resolution radiometer[J]. Vegetatio, 1985, 64: 3-14. doi: 10.1007/BF00033449
[19]	Hill M J, Donald G E. Estimating spatio-temporal patterns of agricultural productivity in fragmented landscapes using AVHRR NDVI time series[J]. Remote Sensing of Environment, 2003, 84: 367-384. doi: 10.1016/S0034-4257(02)00128-1
[20]	Reed B C, Brown J F, Vanderzee D, et al. Measuring phenological variability from satellite imagery[J]. Journal of Vegetation Science, 1994b, 5: 703-714. doi: 10.2307/3235884
[21]	Townshend J R G, Goff T E, Tucker C J. Multitemporal dimensionality of images of normalized difference vegetation index at continental scales[J]. Remote Sensing, 1985, 23: 888-895.
[22]	Tucker C J, Holben B N, Elgin J H, et al. Remote sensing of total dry-matter accumulation in winter wheat[J]. Remote Sensing Environment, 1981, 11: 171-189. doi: 10.1016/0034-4257(81)90018-3
[23]	Calera A, González-Piqueras J, Melia J. Monitoring barley and corn growth from remote sensing data at field scale[J]. International Journal of Remote Sensing, 2004, 25: 97-109. doi: 10.1080/0143116031000115319
[24]	Diallo O, Diouf A, Hanan N P, et al. AVHRR monitoring of savanna primary production in Senegal, West Africa: 1987-1988[J]. International Journal of Remote Sensing, 1991, 12: 1259-1279. doi: 10.1080/01431169108929725
[25]	Forzieri G, Feyen L, Cescatti A, et al. Spatial and temporal variations in ecosystem response to monsoon precipitation variability in southwestern North America[J]. JGR Biogeosciences, 2014, 119: 1999-2017. doi: 10.1002/2014JG002710
[26]	Goward S N, Dye D G. Evaluating North American net primary productivity with satellite observations[J]. Advances in Space Research, 1987, 7: 165-174. doi: 10.1016/0273-1177(87)90308-5
[27]	Rasmussen M S. Developing simple, operational, consistent NDVI-vegetation models by applying environmental and climatic information[J]. International Journal of Remote Sensing, 1998, 19: 97-117. doi: 10.1080/014311698216459
[28]	Running S W, Nemani R R. Relating seasonal patterns of the AVHRR vegetation index to simulated photosynthesis and transpiration of forests in different climates[J]. Remote Sensing of Environment, 1988, 24: 347-367. doi: 10.1016/0034-4257(88)90034-X
[29]	Dutrieux L P, Bartholomeus H, Herold M, et al. Relationships between declining summer sea ice, increasing temperatures and changing vegetation in the Siberian Arctic tundra from MODIS time series (2000-11)[J]. Environmental Research Letters, 2012, 7: 12.
[30]	Yang L, Wylie B K, Tieszen L L, et al. An analysis of relationships among climate forcing and time-integrated NDVI of grasslands over the US northern and central Great Plains[J]. Remote Sensing of Environment, 1998, 65: 25-37. doi: 10.1016/S0034-4257(98)00012-1
[31]	Jin J, Wang Q. Assessing ecological vulnerability in western China based on time-integrated NDVI data[J]. Journal of Arid Land, 2016, 8: 533-545. doi: 10.1007/s40333-016-0048-1
[32]	Reed B C, Brown J F, Vanderzee D, et al. Measuring phenological variability from satellite imagery[J]. Journal of Vegetation Science, 1994, 5: 703-714. doi: 10.2307/3235884
[33]	Verrelst J, Camps-Valls G, Munoz-Mari J, et al. Optical remote sensing and the retrieval of terrestrial vegetation bio-geophysical properties: A review[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2015, 108: 273-290. doi: 10.1016/j.isprsjprs.2015.05.005
[34]	Le Maire G, Marsden C, Nouvellon Y, et al. MODIS NDVI time-series allow the monitoring of Eucalyptusplantation biomass[J]. Remote Sensing of Environment, 2011, 115: 2613-2625. doi: 10.1016/j.rse.2011.05.017
[35]	张婷, 薛东剑, 段金亮, 等. 2000—2019嘉陵江流域植被覆盖时空变化特征及气候响应分析[J]. 长江流域资源与环境, 2021, 30(5): 1110-1120.
	[Zhang Ting, Xue Dongjian, Duan Jinliang, et al. Spatio-temporal variation characteristics and climate response analysis of vegetation coverage in Jialing River Basin from 2000 to 2019[J]. Resources and Environment in the Yangtze Basin, 2021, 30(5): 1110-1120.]
[36]	陈婷, 夏军, 邹磊, 等. 白洋淀流域NDVI时空演变及其对气候变化的响应[J]. 资源科学, 2021, 43(6): 1248-1259. doi: 10.18402/resci.2021.06.15
	[Chen Ting, Xia Jun, Zou Lei, et al. Spatiotemporal variations of NDVI of different vegetation types in the Baiyangdian Basin under the background of climate change[J]. Resources Science, 2021, 43(6): 1248-1259.] doi: 10.18402/resci.2021.06.15
[37]	Brown C E. Coefficient of Variation, Applied Multivariate Statistics in Geohydrology and Related Sciences[M]. Springer, Berlin: Heidelberg, 1998.
[38]	位宏, 李晓蕾, 徐丽萍, 等. 玛纳斯河流域NDVI时空变化及对气象因子的响应[J]. 水土保持研究, 2019, 26(1): 215-220.
	[Wei Hong, Li Xiaolei, Xu Liping, et al. Spatial and temporal distribution of NDVI and its response to climate factors in the Manasi River Basin[J]. Research of Soil and Water Conversation, 2019, 26(1): 215-220.]
[39]	陆妍, 喻文兵, 郭明, 等. 黑龙江省漠河地区土地覆被与地表温度时空变化特征研究[J]. 冰川冻土, 2017, 39(5): 1137-1149.
	[Lu Yan, Yu Wenbin, Guo Ming, et al. Spatiotemporal variation characteristics of land cover and land surface temperature in Mohe County, Helongjiang Province[J]. Journal of Glaciology and Geocryology, 2017, 39(5): 1137-1149.]
[40]	Adam J C, Hamlet A F, Lettenmaier D P. Implications of global climate change for snowmelt hydrology in the twenty-first century[J]. Hydrological Processes, 2009, 23(7): 962-972. doi: 10.1002/hyp.7201
[41]	Gu Y, Wylie B K. Detecting ecosystem performance anomalies for land management in the upper Colorado River Basin using satellite observations, climate data, and ecosystem models[J]. Remote Sensing, 2010, 2: 1880-1891. doi: 10.3390/rs2081880
[42]	Jiapaer G, Chen X, Bao A M. A comparison of methods for estimating fractional vegetation cover in arid regions[J]. Agricultural and Forest Meteorology, 2011, 151: 1698-1710. doi: 10.1016/j.agrformet.2011.07.004

北疆地区草地TI-NDVI与NDVImax时空异质性评价

Spatio-temporal heterogeneity evaluation of grassland TI-NDVI and NDVImax in northern Xinjiang

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